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A multi-scale channel-wise convolution-based multi-level heat stress assessment

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Abstract

In the previous works on heat stress detection, various state-of-the-art machine learning techniques had been used to detect stress patterns from electroencephalographic (EEG) signals. Since the handcrafted feature engineering-based approaches pose certain limitations and are sensitive to transform the nonlinearities of EEG, deep learning techniques have drawn attention in the domain of EEG-based applications. Moreover, existing approaches for stress detection consider the whole frequency band (delta to gamma) which conceals the redundant and lossy information that increased the false detection rate. Also, these approaches were implemented only to identify the stress in binary classes and confined their application to identifying the level of stress. Therefore, a multi-scale channel-wise convolution-based multi-level heat stress assessment has been proposed in this paper. The novel contributions of this work are (1) designing a multi-scale convolutional neural network (MS-CNN) for extracting precise information from individual frequency bands of EEG, (2) use of sparse connectivity to reduce the redundant information and increase the performance with less trainable parameters, and (3) multi-level heat stress assessment for precise identification of the severity of stress. The proposed approaches were evaluated on pre-recorded data of 10 rats in a simulated laboratory environment. The high accuracy of approximately 96–97% and 90–92% has been achieved for binary and multi-level stress detection. There is approximately a 6 to 50% reduction in the trainable parameters with a 2% improvement of accuracy achieved on adopting channel-wise convolution over MS-CNN and deep convolutional neural network (DCNN). This demonstrates the effectiveness of the adopted multiscale feature extraction and sparse connectivity to improve the performance with a less complex model.

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References

  1. Pogačar T, Casanueva A, Kozjek K, Ciuha U, Mekjavić IB, Bogataj LK, Črepinšek Z (2018) The effect of hot days on occupational heat stress in the manufacturing industry: implications for workers’ well-being and productivity. Int J Biometeorol 62(7):1251–1264

    Article  Google Scholar 

  2. Upadhyay PK, Nagpal C (2020) PCA aided FCM Identifies stressful events of sleep EEG under hot environments. IETE J Res 59:1–14

    Article  Google Scholar 

  3. Upadhyay PK, Nagpal C (2020) Time-frequency analysis and fuzzy-based detection of heat-stressed sleep EEG spectra. Med Bio Eng Comput 59:1–17

    Google Scholar 

  4. Upadhyay PK, Nagpal C (2020) Sleep stage and heat stress classification of rodents undergoing high environmental temperature. In: Computational methods and data engineering, pp 577–587. Springer, Singapore

  5. Jebelli H, Hwang S, Lee S (2018) EEG-based workers’ stress recognition at construction sites. Autom Constr 93:315–324

    Article  Google Scholar 

  6. Xu Q, Nwe TL, Guan C (2014) Cluster-based analysis for personalized stress evaluation using physiological signals. IEEE J Biomed Health Inf 19(1):275–281

    Article  Google Scholar 

  7. Sani MM, Norhazman H, Omar HA, Zaini N, Ghani SA. Support vector machine for classification of stress subjects using EEG signals. In: Proceedings of 2014 IEEE conference on systems. process and control (ICSPC), 2014, pp 127–131

  8. Sinha RK (2003) Artificial neural network detects changes in electroencephalogram power spectrum of different sleep-wake states in an animal model of heat stress. Med Biol Eng Comput 41:595–600

    Article  Google Scholar 

  9. Nagpal C, Upadhyay PK (2018) Adaptive neuro fuzzy inference system technique on polysomnographs for the detection of stressful conditions. IETE J Res 65(3):298–309

    Article  Google Scholar 

  10. Upadhyay PK, Nagpal C (2020) Wavelet based performance analysis of SVM and RBF kernel for classifying stress conditions of sleep EEG. Sci Technol 23(3):292–310

    Google Scholar 

  11. Sinha RK (2004) Electro-encephalogram disturbances in different sleep-wake states following exposure to high environmental heat. Med Biol Eng Comput 42:282–287

    Article  Google Scholar 

  12. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444

    Article  Google Scholar 

  13. Sun K, Zhang J, Zhang C, Hu J (2017) Generalized extreme learning machine autoencoder and a new deep neural network. Neurocomputing 230:374–381

    Article  Google Scholar 

  14. Badrinarayanan V, Kendall A, Cipolla R (2017) Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 39(12):2481–2495

    Article  Google Scholar 

  15. Hannun AY, Rajpurkar P, Haghpanahi M, Tison GH, Bourn C, Turakhia MP, Ng AY (2019) Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nat Med 25(1):65

    Article  Google Scholar 

  16. Acharya UR, Fujita H, Lih OS, Hagiwara Y, Tan JH, Adam M (2017) Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network. Inf Sci (Ny) 405:81–90

    Article  Google Scholar 

  17. Kshirsagar GB, Londhe ND (2018) Improving performance of Devanagari script input-based P300 speller using deep learning. IEEE Trans Biomed Engg 66(11):2992–3005

    Article  Google Scholar 

  18. Kshirsagar GB, Londhe ND (2019) Weighted ensemble of deep convolution neural networks for single-trial character detection in devanagari-script-based P300 speller. IEEE Trans Cogn Devel Syst 12(3):551–560

    Article  Google Scholar 

  19. Chaudhary S, Taran S, Bajaj V, Sengur A (2019) Convolutional neural network-based approach towards motor imagery tasks EEG signals classification. IEEE Sensors 19(12):4494–4500

    Article  Google Scholar 

  20. Chen S, Zhao Q (2018) Shallowing deep networks: Layer-wise pruning based on feature representations. IEEE Trans Pattern Anal Mach Intell 41(12):3048–3056

    Article  Google Scholar 

  21. Lawhern VJ, Solon AJ, Waytowich NR, Gordon SM, Hung CP, Lance BJ (2018) EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces. J Neural Eng 15(5):056013

    Article  Google Scholar 

  22. Dey PK (1998) Modification of dopamine receptor agonist mediated behavioral responses in rats following exposure to chronic heat stress. Biomedicine 18(1):41–47

    Google Scholar 

  23. Dey PK (2000) Involvement of endogenous opiates in heat stress. Biomedicine 20(2):143–148

    Google Scholar 

  24. Sharma HS, Westman J., Nyberg F (1998) Pathophysiology of brain edema and cell changes following hyperthermic brain injury. In: Sharma HS, Westman J (Eds). Progress in brain research, Elsevier, Amsterdam, vol. 115: 35–412

  25. https://www.coursera.org/lecture/convolutional-neural-networks/networks-in-networks-and-1x1-convolutions-ZTb8x

  26. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D et al (2015) Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1–9

  27. Iandola FN, Han S, Moskewicz MW, Ashraf K, Dally WJ, Keutzer K (2016) SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size. arXiv preprint arXiv:1602.07360

  28. Lin M, Chen Q, Yan S (2013) Network in network. arXiv preprint arXiv:1312.4400

  29. Clevert DA, Unterthiner T, & Hochreiter S (2015) Fast and accurate deep network learning by exponential linear units (elus). arXiv preprint arXiv:1511.07289

  30. Kshirsagar GB, Londhe, N D (2020) Increasing the usability of the Devanagari script input based speller. In: Bajaj V, Sinha GR (ed) Modelling and analysis of active biopotential signals in healthcare, Volume 2, IOP Publishing, Bristol

  31. Tieleman T, Hinton G (2012) Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude. COURSERA Neural Netw Mach Learn 4(2):26–31

    Google Scholar 

  32. Chollet F (2015) Keras. www.Keras.io

  33. Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J et al (2016) Tensorflow: A system for large-scale machine learning, In 12th {USENIX} symposium on operating systems design and implementation ({OSDI} 16), pp 265–283

  34. Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks, In Proceedings of the thirteenth international conference on artificial intelligence and statistics, pp 249–256

  35. Srivastava N, Geoffrey H, Alex K, Ilya S, Ruslan S (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929–1958

    MathSciNet  MATH  Google Scholar 

  36. Sinha RK (2008) EEG power spectrum and neural network-based sleep-hypnogram analysis for a model of heat stress. J Clin Monit Comput 22(4):261

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Engineering Department, Al Jazirah Institute of Science and Technology, ADVETI, Abu Dhabi, UAE

    Chetna Nagpal

  2. Department of Electrical and Electronics Engineering, Birla Institute of Technology, Mesra, Ranchi, India

    Prabhat Kumar Upadhyay

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  1. Chetna Nagpal
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Nagpal, C., Upadhyay, P.K. A multi-scale channel-wise convolution-based multi-level heat stress assessment. Neural Comput & Applic 34, 19181–19191 (2022). https://doi.org/10.1007/s00521-022-07518-5

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